# Test the ViDiDetect Tool

1. From the View Browser, click Add and navigate to your testing images. These should be a selection of passing and failing images. Ideally you will also have denoted the state of the part in the image's file name.

2. For this example, in the Display filter within the View Browser, type in 'OK' and press enter to only display the images that are appended with the letters "OK".

3. From the Actions for N Views menu, select Label Views and apply the Good labels to these images.

4. In the Display filter within the View Browser, type in 'NG' and press enter to only display the images that are appended with the letters "NG".

5. From the Actions for N Views menu, select Label Views and apply the Bad labels to these images.

6. With the images all labeled, you will now get statistical data on all of the images (available in the Database Overview panel), which will allow you to assess the state of the previously trained neural network model.

7. In this example, the Feature Size parameter will be adjusted to attempt to better attenuate the neural network Model. This can be accomplished by either adjusting the numeric value in the Feature Size parameter itself, or utilizing the Feature Size graphical element within the Image Display Area. If you adjust either, they will be synchronized to reflect the change.

8. By adjusting the Feature Size, the neural network model has been invalidated and will need to be retrained. Before doing so, the Training Set parameter will be adjusted (the percentage will be adjusted from 50% to 70% to increase the number of known good images used in the Training Set), as well as increasing the Epoch Count parameter. After adjusting the parameters, press the Brain icon to train the tool again.

   Note: For more information on these parameters, refer to the Training Tool Parameters topic.

9. After the tool has been retrained, it will be time to review the results in the Database Overview again.

10. In this example, the retrained neural network model has a much better divide between the images classified as Good and Bad. Adjusting the Threshold parameters to account for the difference should account for the images classified as Inter.

11. With the neural network model trained, it is now time to return to the spreadsheet and further assess the tool's performance by running testing images via the Filmstrip to assess the performance of the fixture and tool.